将模型中的多目标优化部分转换成景点之间的相似度，修改后的完整代码

下面是一个完整的例子，展示如何将模型中的多目标优化部分转换成景点之间的相似度的代码实现：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset

class SceneDataset(Dataset):
    def __init__(self, scene_feats, scene_sim):
        self.scene_feats = scene_feats
        self.scene_sim = scene_sim
        
    def __len__(self):
        return len(self.scene_feats)
    
    def __getitem__(self, idx):
        return idx, self.scene_feats[idx], self.scene_sim[idx]
    
class MyModel(nn.Module):
    def __init__(self, num_scenes, scene_feat_dim, hidden_dim):
        super(MyModel, self).__init__()
        self.num_scenes = num_scenes
        self.scene_feat_dim = scene_feat_dim
        self.hidden_dim = hidden_dim
        
        # 定义景点特征矩阵
        self.scene_feats = nn.Parameter(torch.rand(num_scenes, scene_feat_dim))
        
        # 定义线性层
        self.linear = nn.Linear(scene_feat_dim, hidden_dim)
        
    def forward(self, scene_ids):
        # 获取当前batch中所有景点的特征
        batch_scene_feats = self.scene_feats[scene_ids]
        # 将景点特征通过线性层映射到隐空间
        hidden = self.linear(batch_scene_feats)
        # 计算相似度矩阵
        sim_matrix = torch.matmul(hidden, hidden.t()) / (hidden.norm(dim=1).unsqueeze(1) * hidden.norm(dim=1))
        # 最大化相似度矩阵中的值
        loss = -sim_matrix.mean()
        return loss
    
def train(model, train_loader, optimizer, device):
    model.train()
    total_loss = 0.0
    for batch_idx, _, scene_sim in train_loader:
        batch_size = len(batch_idx)
        scene_ids = batch_idx.to(device)
        scene_sim = scene_sim.to(device)
        optimizer.zero_grad()
        loss = model(scene_ids)
        loss.backward()
        optimizer.step()
        total_loss += loss.item() * batch_size
    return total_loss / len(train_loader.dataset)

def test(model, test_loader, device):
    model.eval()
    total_loss = 0.0
    with torch.no_grad():
        for batch_idx, _, scene_sim in test_loader:
            batch_size = len(batch_idx)
            scene_ids = batch_idx.to(device)
            scene_sim = scene_sim.to(device)
            loss = model(scene_ids)
            total_loss += loss.item() * batch_size
    return total_loss / len(test_loader.dataset)

# 生成随机数据
num_scenes = 100
scene_feat_dim = 50
hidden_dim = 10
batch_size = 32
train_size = int(num_scenes * 0.8)
test_size = num_scenes - train_size
scene_feats = torch.randn(num_scenes, scene_feat_dim)
scene_sim = torch.rand(num_scenes, num_scenes)

# 划分训练集和测试集
train_dataset = SceneDataset(scene_feats[:train_size], scene_sim[:train_size, :train_size])
test_dataset = SceneDataset(scene_feats[train_size:], scene_sim[train_size:, :train_size])
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size)

# 定义模型和优化器
model = MyModel(num_scenes, scene_feat_dim, hidden_dim)
optimizer = optim.Adam(model.parameters(), lr=0.01)

# 训练模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
num_epochs = 10
for epoch in range(num_epochs):
    train_loss = train(model, train_loader, optimizer, device)
    test_loss = test(model, test_loader, device)
    print('Epoch {}, Train Loss: {:.4f}, Test Loss: {:.4f}'.format(epoch+1, train_loss, test_loss))

在这个例子中，我们首先定义了一个`SceneDataset`类，用于存储景点的特征和相似度矩阵。`MyModel`类则是我们定义的模型，其中包括一个`nn.Parameter`类型的变量`scene_feats`，表示所有景点的特征，以及一个线性层，用于将景点特征映射到隐空间。在`forward`函数中，我们首先获取当前batch中所有景点的特征，然后将景点特征通过线性层映射到隐空间，接着计算相似度矩阵，最后将优化目标修改为最大化相似度矩阵中的值。

在训练过程中，我们将每个batch中的景点特征和相似度矩阵作为输入，使用Adam优化器进行优化。在每个epoch结束后，我们计算训练集和测试集上的平均损失。

需要注意的是，这里使用的余弦相似度只是一种相似度度量方法，实际应用中需要根据具体问题选择适合的相似度度量方法。